Soil salinity is a major constraint to soil health and crop productivity, especially in arid and semi-arid regions. The most accurate measurement of soil salinity is considered to be the electrical conductivity of saturated soil extracts (EC_e). Because this method is labor-intensive, it is unsuitable for routine analysis in large soil sampling campaigns. This study aimed to identify the best models to estimate soil salinity based on EC_e in relation to a rapid electrical conductivity (EC) measurement in soil/water (referred to as S:W henceforward) extracts. We evaluated the relationship between EC_e and the EC_S:W extract ratios (1:1, 1:2, and 1:5) in salt-affected soils from the semi-arid Sehb El Masjoune region of Morocco. The soil salinity in this region is 0.5 to 235 dS/m, as determined by the EC_e method. A total of 125 soil samples, from topsoil (0–15 cm) and subsoil (15–30 cm) with mainly fine to medium textures, were analyzed using linear, logarithmic, and second-order polynomial regression models. The models included all samples or grouped samples according to soil texture (fine, medium) or specific textural classes. The mean EC_e values were 2.6, 3.1, and 7.9 times greater than the EC of 1:1, 1:2, and 1:5 S:W extracts, respectively. Polynomial regression models had the best predictive accuracy, R² = 0.98, and the lowest root mean square error of 10.6 to 10.7 dS/m for the EC_S:W extract ratios of 1:5 and 1:2. The polynomial models could represent the non-linear relationships between EC_e and salinity indicators, especially in the 80–170 dS/m salinity range, where other models typically underestimate the salinity. These results confirm that advanced regression techniques are suitable for predicting soil salinity in a salt-affected semi-arid region. The site-specific models outperformed previously published models, because they consider the spatial variability and heterogeneity of the salinity in the study area explicitly. This confirms the importance of calibrating soil salinity models according to the local soil and environmental conditions. Consequently, we can undertake soil salinity assessments in hundreds of samples by using the simple, rapid EC_S:W extraction method as a direct indicator of EC and extrapolate to EC_e with a polynomial regression model. Our approach enables the widespread soil salinity assessments that are needed for land-use planning, irrigation management, and crop selection in salt-affected landscapes.

Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to map soil salinity at field scale in the semi-arid Sehb El Masjoune area, central Morocco. A total of 26 soil samples were analyzed for Electrical Conductivity (EC), and 500 Apparent Electrical Conductivity (ECa) measurements were collected and calibrated using the field samples. Spectral and topographic covariates derived from Unmanned Aerial Vehicle (UAV) and PlanetScope imagery supported model training using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and a Stacked Ensemble Learning Model (ELM). Regression Kriging (RK) was applied to model residuals to improve spatial prediction. ELM achieved the highest accuracy (R² = 0.87, RMSE ≈ 4.15), followed by RF, which effectively captured nonlinear spatial patterns. RK improved PLSR accuracy (by 11.1% for PlanetScope, 13.8% for UAV) but offered limited gains for RF, SVR, and ELM. SHAP analysis identified topographic covariates as the most influential predictors. Both UAV and PlanetScope delineated similar saline–sodic zones. The study demonstrates the following: (1) a scalable, data-efficient workflow for salinity mapping; (2) model and RK performance depend more on algorithmic design than sensor type; (3) interpretable ML and spatial modeling enhance understanding of salinity processes in semi-arid systems.

Phosphate is a crucial non-renewable mineral resource, mainly utilized in producing fertilizers that support global agriculture. As phosphorus is an indispensable nutrient for plant growth, phosphate holds a key position in ensuring food security. While deposits are distributed worldwide, the largest reserves are concentrated in Morocco. The Benguerir phosphate mining in Morocco generates heterogeneous waste (i.e., including overburden, tailings, and phosphogypsum) that complicates management and valorization, which is the beneficial reuse or value recovery from waste materials (e.g., use in cover systems, buffering, or other engineered applications). Therefore, it is essential to characterize their mineralogical properties to evaluate their environmental impact and possibilities for reuse or site revegetation. To do so, we integrate VNIR–SWIR reflectance spectroscopy with HandHeld X-ray fluorescence (HHXRF) to characterize phosphate waste rock and assess its reuse potential. For this purpose, field samples (n = 104) were collected, and their spectral reflectance was measured using an ASD FieldSpec 4 spectroradiometer (350–2500 nm) under standardized laboratory conditions. Spectra were processed (Savitzky–Golay smoothing, convex-hull continuum removal) and matched to ECOSTRESS library references; across the dataset, library matching achieved mean RMSE = 0.15 ± 0.053 (median 0.145; 0.085–0.350), median SAM = 0.134 rad, median SID = 0.029, and mean R² = 0.748 ± 0.170, with 84% of spectra yielding R² > 0.70. In parallel, HHXRF major and trace elements were measured on all samples to corroborate spectral interpretations. Together, these analyses resolve carbonate–clay–phosphate assemblages (dolomite commonly dominant, with illite/smectite–kaolinite, quartz, and residual carbonate-fluorapatite varying across samples). Elemental ratios (e.g., Mg/Ca distinguishing dolomite from calcite; K/Al indicating illite) reinforce spectral trends, and phosphate indicators delineate localized enrichment (P₂O₅ up to 23.86 wt % in apatite-rich samples). Overall, the combined workflow is rapid, low-impact, and reproducible, yielding coherent mineralogical patterns that align across spectroscopic and geochemical lines of evidence and providing actionable inputs for selective screening, targeted material reuse, and more sustainable mine reclamation planning.